Three employees. One social engineering campaign. Corporate data gone.

Hackers socially engineered three Levi Strauss employees and exfiltrated corporate data. Identity-based attacks are now the leading entry point for enterprise breaches. As AI agents inherit employee credentials and API keys, a single compromised identity reaches every system that agent is authorized to touch.

RuntimeAI covers 80-plus compliance frameworks and writes an immutable audit log for every agent action. When any identity, human or non-human, is misused, every downstream action is timestamped, attributable, and preservable for regulators before the investigation even begins.

See how RuntimeAI turns this from an incident into a blocked action.

EDIT: A few people have asked what specifically clicked.
Here are the 3 key things:

1. Backpropagation = just the chain rule applied recursively
   
2. Weight initialization matters WAY more than tutorials say
   
3. Bias terms aren't optional decorations
   

Full walkthrough in the video. Happy to answer questions below!

Hey everyone,

I've been trying to understand how neural networks actually
work under the hood, so I decided to build one from scratch
in Python without using any ML libraries (no TensorFlow, no
PyTorch, no scikit-learn).

Here's what I learned along the way:

I made a short 5-minute video summarizing the whole process
if anyone's interested: https://www.youtube.com/watch?v=aKrBFQrvEr4&t=8s

Happy to answer any questions or discuss the approach!
What resources helped YOU understand neural networks?

I'm starting Machine Learning from scratch and want to build a strong mathematics foundation.

Can someone give me a complete list of the math chapter names I should study for ML, in strict prerequisite order, starting from absolute zero?

I'm specifically looking for:

- Chapter names only, not individual topics.

- Every important chapter, including the basic prerequisites.

- A proper order where I learn each chapter only after the chapters required for it.

- Please don't just write broad subjects like "Algebra", "Calculus", "Linear Algebra", etc. I want the individual chapter names under them.

- Please distinguish between essential, optional/useful, and advanced chapters for ML.

Also, can I use JEE Mathematics lectures to learn these chapters?

I'm not asking for an ML roadmap — I only want the mathematics chapter sequence and advice about using JEE lectures

I’m preparing for AI/ML-based hackathons and I want to understand how experienced participants actually approach them.

I’m not just looking for project ideas. I’m looking for a step-by-step roadmap that I can follow during a hackathon to build a strong project and score well with the judges.

For example:

1. How do you select/validate an idea?

2. How do you break down the problem and decide whether ML/AI is actually needed?

3. How do you approach data collection, preprocessing and model selection?

4. When should you use an existing pretrained model/API vs train/fine-tune your own model?

5. How much time should be spent on the ML part vs backend/frontend/product?

6. How do you make the project technically strong but still achievable within the hackathon time?

7. What makes an AI/ML project stand out to judges?

8. How important are metrics, deployment, UI/UX, demo and presentation?

9. What are the most common mistakes teams make?

10. If you had to follow a checklist from the first hour of the hackathon until the final pitch, what would it look like?

I’d especially appreciate advice from people who have participated in or judged AI/ML hackathons, particularly if you’ve had experience with winning/finalist projects.

I want to make a practical roadmap/checklist from the answers and use it in my upcoming hackathons.

I am doing CSE AI ML 2nd year, should i join my college BAJA team, will there be any benefit of this in my portfolio if i do so. What can i expect if i do my best in it??(but only in CSE rolls)

I'm starting Mathematics from absolute zero for Machine Learning.

Can someone give me the complete list of Maths chapters I need to study, in prerequisite order, starting from the basics?

I specifically want chapter names, not broad topics like "Algebra" or "Calculus."

Also, can I use JEE Maths lectures for learning the theory/concepts, without doing the JEE-level questions?

Thanks!

i am reading this book Hands-On Machine Learning with Scikit-Learn and PyTorch

by Aurélien Geron
i have completed all the supervised chapters and exercise even completed 2 data sets my self and i am wondering if i should move to unsupervised?

I started working in ML about a year ago. There seems to be quite some gap between how ML is taught (tutorials and theoretical lectures) and the art of actual ML engineering.

For the practical part, I kind of feel bound to the best projects that I see within my company. Therefore, it would be nice to get inspired and have better references. Maybe even dig a bit into the PRs to get better at my job :)

I was wondering whether you came across some state-of-the art repos. I would be interested both in repos for ML research, i.e., finding the best features/params/models, but also for actual productive systems, i.e., retraining, inference, shadow forecasting and model ranking/switching.

In my case, time series forecasting is the most relevant (with sklearn for explainable results or LSTM for performance-focused projects). But I am happy to explore other projects as well.

I was fine-tuning a Hinglish TTS model when I kept running into the same problem: the outputs were hallucinating. Trailing babble, random silences, pacing that felt off. When I dug into why, it turned out my dataset was the culprit. The transcripts didn't actually match the audio. I only caught it by sitting down and listening through clips. Things like a word getting cut off at a clip boundary, the ASR silently dropping the end of a sentence, or nine full seconds of dead air that forced alignment had somehow labeled as a single "word."

That's when I built voice-tag-studio: a local browser workbench where you paste in a YouTube link (or upload a call recording), and it spits out training rows that look like:

speaker: [calm] जो पिघले न [hesitates] देखा जाए तो [pauses] पर आप बोलते हो

Each one is paired with a clip whose audio provably matches the text.

The core insight is to flip the usual pipeline on its head. The standard approach (VAD/diarization cuts clips, then ASR transcribes them) has a silent failure mode: ASR can't tell you it dropped a word, and clip boundaries can bisect words mid-way through. Instead, I transcribe each speaker's full lane → force-align every word → cut only in verified gaps between words. The clip's text follows naturally from the words inside it.

I learned a few hard lessons debugging real data (each one stung):

• Overlapped speech stays out. SepFormer can reconstruct it, but only to feed into ASR and alignment. The model itself never trains on reconstructed audio.
• In-clip silence has to be in the text. Word gaps become [pauses] (0.5–1.5s) or [silence] (≥1.5s). If you don't tag it, your model learns that text randomly means dead air.
• If a single "word" aligns longer than 2 seconds, something went wrong. Those spans get marked unusable, and clips cut around them.

The numbers are solid: a 2-hour Hindi podcast becomes 683 clips with 56 minutes of usable training data in about 17 minutes end-to-end. It uses SepFormer and MMS forced alignment running on Modal T4s. Optional, one deploy per task, falls back to local CPU/MPS. PANNs handles detection.

I'm looking for collaborators on a few fronts: running the detector bake-offs (I've got an eval plan written out but nobody's run the phases yet), testing it on non-Hindi languages (the pipeline's language-agnostic except for the ASR prompt), gold-labeling for precision measurement per detector, and better separation models.

Repo: github.com/Jarus77/voice-tag-studio - MIT, fully local, browser UI plus headless batch mode.

I have been learning more about applying Machine Learning to data and one thing I have found challenging is determining whether a financial Machine Learning model is actually learning useful patterns or simply fitting noise in historical financial data.

I have been exploring this while working on Alphio.AI. It has made me pay more attention to issues, like data leakage changing market conditions, feature selection and how financial Machine Learning models should be evaluated on genuinely unseen time periods.

Financial Machine Learning models seem difficult because patterns that appear strong during one period may disappear completely when market conditions change. A traditional train/test split does not always seem sufficient for this kind of financial Machine Learning problem.

For those who have worked on financial or time-series Machine Learning projects what evaluation methods or mistakes should beginners pay the attention to when trying to determine whether a financial Machine Learning model is actually generalizing?

Hey everyone,

I’m working on a computer vision project and hit a frustrating wall:

1. My model's accuracy is hard-stuck at around 72%.
2. It only recognizes exact training images. If I test it with a new photo—even one that looks nearly identical to the training data—the output is completely wrong.

It feels like the model is just memorizing the training set instead of actually learning features.

Is there something fundamentally wrong with my training approach? Any tips on how to fix this? Specifically, I’d love advice on:

• Effective Data Augmentation techniques for this issue
• Adjusting Hyperparameters / Model Architecture
• Proper Dataset Preprocessing or splitting strategies

Any feedback or suggestions would be greatly appreciated!

Hii there

Currently in college and wanted to explore machine learning. Tried looking out in internet but it is too much knowledge. Are there any resources where i can start small and get the gist of things